Fireball streak detection with minimal CPU processing requirements for the Desert Fireball Network data processing pipeline

Abstract

The detection of fireballs streaks in astronomical imagery can be carried out by a variety of methods. The Desert Fireball Network--DFN--uses a network of cameras to track and triangulate incoming fireballs to recover meteorites with orbits. Fireball detection is done on-camera, but due to the design constraints imposed by remote deployment, the cameras are limited in processing power and time. We describe the processing software used for fireball detection under these constrained circumstances. A cascading approach was implemented, whereby computationally simple filters are used to discard uninteresting portions of the images, allowing for more computationally expensive analysis of the remainder. This allows a full night's worth of data; over 1000 36 megapixel images to be processed each day using a low power single board computer. The algorithms chosen give a single camera successful detection large fireball rate of better than 96 percent, when compared to manual inspection, although significant numbers of false positives are generated. The overall network detection rate for triangulated large fireballs is estimated to be better than 99.8 percent, by ensuring that there are multiple double stations chances to detect one fireball.

arXiv comment20 pages, 5 figures

References in corpus (4)

• scikit-image: Image processing in Python
• Data Mining and Machine Learning in Astronomy
• The Dingle Dell meteorite: a Halloween treat from the Main Belt
• Comparing Analytical and Numerical Approaches to Meteoroid Orbit Determination using Hayabusa Telemetry

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